1. Field of the Invention
The present invention generally relates to the restaurant industry, and, more particularly, to a system and method of real-time electronic prediction and management to of food product demand, especially in quick-service restaurant industry.
2. Description of Related Art
The quick-service (or fast food) restaurant industry's primary value proposition is speed-of-service—i.e., how quickly the restaurant can deliver a complete meal after a customer has placed an order. Quick-service restaurant operations are built upon the concept of preparing a limited menu of food product before customers place their orders. By preparing food ahead of time and keeping it warm in a holding buffer, restaurant employees can quickly grab food product from the buffer, bag it, and hand it to a customer. This faster speed-of-service enables quick-service restaurants to serve many more customers during busy mealtimes than a traditional sit-down restaurant.
In order to efficiently serve the customers (e.g., during a “rush hour”), a restaurant manager must carefully manage the “buffer level”—i.e. the amount of each food product that employees make and store in the holding buffer throughout the day. The restaurant manager needs to ensure that sufficient food product is on hand to meet anticipated demand, but must also ensure that the food product doesn't sit too long before being served, as food product quickly deteriorates after being prepared. Ideally, restaurant managers would like to buffer exactly the right amount of food product to serve consumers over the next few minutes. Unfortunately, it's difficult for managers to know how many consumer orders they will receive over the next few minutes, so they are forced to buffer “extra” product, just to be safe.
The buffer management problem is fundamentally a trade-off between the quality of the restaurant's food and the speed with which the restaurant can serve customers. The management of this trade-off is one of the largest drivers of the restaurant's profitability, as speed-of-service drives sales as long as quality is maintained. Despite its importance to the industry, restaurant managers can do little more than make “educated guesses” as they lack one critical piece of information—when will the consumers arrive?
The restaurant industry's state-of-the art solutions are based on data-mining historical sales information to predict future ordering patterns. Each restaurant saves a multi-week history of its sales volumes for each product. For example, sales data of the past 70 days may be stored and analyzed. The data-mining software then averages product volumes for discrete time windows (called “day parts”) throughout the day—e.g., 15 or 30 minute windows are common, for each day of the week. The day-parts printout might suggest, for example, keeping 2 bins of patties on hand from 11:00 to 11:30, and then increasing the buffer to 3 bins from 11:30 to 12:00:
In another approach to buffer management, a program makes the determination of when to buffer completed sandwiches based on the same theory of analyzing historical sales data, except that, in this approach, the sales data for the sale in the last few minutes of popular completed sandwiches (e.g., cheeseburgers) is reviewed. Thereafter, the program calculates the number of completed cheeseburgers to buffer as a function of the number of cheeseburgers sold within the last few minutes. Thus, again, the food production decision is based upon historical information (i.e., sales that have already occurred).
The current approach is based on the underlying assumption that “future product demand will be very similar to historic (or past) product demand”. This assumption is relatively true when product demand is averaged over large time windows, for example over a day. Restaurants can have relatively high confidence that they will sell roughly the same number of cheeseburgers today as they did yesterday—assuming that prices do not change.
However, the current approach does not allow restaurant resources to be managed on a real-time basis because the variability of the correlation between past and future demand events is too large. In other words, historic information does not allow restaurant managers to know with confidence the demand that their restaurant will see over the next several minutes; however, restaurant performance (speed and quality) would benefit significantly if product demand could be predicted accurately within the secondary shelf life of the restaurant's food products.
The current approach suffers because it infers future demand from historic demand, rather than taking a direct measurement of future demand. The reliability of the inference (i.e., the correlation between the inferred demand and the actual demand) depends upon the time window under consideration. The current approach works fairly well when demand is correlated across large time windows (e.g., predicting the number of patties that will be consumed across a day). The current approach becomes progressively less accurate as the time window shrinks. Further, current “inventory management systems” are inadequate to the needs of the restaurant industry because they do not account for the limited secondary shelf life of food products.
As a result, data mining of historical sales generates demand estimates with relatively large ranges. For example, in one restaurant, data mining may result in a prediction that for peak periods of the day the kitchen staff should pre-produce 47 bins of burger patties. If each bin holds 18 patties, the range of production is 72 to 126 patties. Such production predictions may be accurate (i.e., the predictions may handle the customer demand during the peak periods) only because the range (which is 126−72=54 patties) is so large. Unfortunately, large variances leave the restaurant vulnerable to over-production and, in practice, provide little more than rough production guidelines.
The area of management of food processing and food production facilities, such as the preparation of frozen dinners, differs significantly from the production problems in quick-service restaurants because it is not a real-time management of food processing and food production. Processing facilities schedule production and make production decisions based upon sales forecasts that are weeks and months ahead. Production times and rates can vary far more than in quick-service restaurants, with little effect, as the output of the facility is destined for warehousing and distribution facilities. Where quick-service restaurants must make minute-by-minute decisions, production facilities can often afford to make daily, or even weekly production decisions. Thus, the needs of production facilities and quick-service restaurants vary greatly. The decisions to be taken at a food processing facility are not impacted by the minute-by-minute changes in the demand for that facility's products.
Therefore, it is desirable to improve a restaurant's demand prediction accuracy so as to enable restaurant mangers to use their production resources more efficiently—increasing same store profitability by improving speed-of-service, food product quality, and reducing food product wastage. More accurate demand prediction enables restaurant operations to:                (1) Avoid underproduction because under-production slows the restaurant's speed-of-service. When the buffer runs out of a certain food product, then customers must add the food production time (often a few minutes) to their wait time. This is especially problematic for a serial queue, like a drive-thru where every customer in line must add the food production time to his or her wait time. Underproduction can seriously damage the restaurant's profitability by reducing the number of customers served during peak meal times.        
(2) Avoid over-production because over-production reduces the restaurant's food quality and increases wastage, as food product spends too much time in the bin. If the food product's bin time exceeds the secondary shelf life, then it must be wasted.
(3) Minimize bin time because minimizing bin times means hotter, fresher food products—a well-known market share driver. Restaurants would prefer to minimize the amount of time that food products spend in the buffer.
(4) Pre-produce completed products because accurately buffering completed products can significantly drive the restaurant's sales by improving the restaurant's speed-of-service. Restaurants can buffer both food product components (e.g., burger patties) and completed food products (e.g., plain cheeseburgers); however, the secondary shelf life of a completed food product is much shorter (2-3 minutes) than that of a food product component (30 minutes).
(5) Reduce “wasteful” production when a restaurant attempts to buffer completed food products based on the historical sales data approach. This method is open to a significant number of incorrect guesses, which not only waste the food product (e.g., unused sandwiches or cheeseburgers), but also consume valuable production time that was allocated to making a product that no one used.
As discussed before, a fundamental limitation of the current approach is that the analysis of historical data only infers a range of probable future demand. It is therefore further desirable to improve the accuracy of future demand prediction using a method that moves beyond inference—to a direct measurement of demand or leading indicators of demand. In other words, it is desirable to base future food production decisions on the number of customers currently on the restaurant property who have not yet placed orders (i.e., sales that have not yet occurred, but are likely to occur within the next several minutes). Direct measurements of demand will enable restaurants to improve the economic efficiency of their real-time production operations—ensuring that production resources are assigned to profit-enhancing tasks.